Anonymous Gossip Improving Multicast Reliability in Mobile Ad-Hoc Networks

[转载]Gossip算法学习1. 概述gossip，顾名思义，类似于流⾔传播的概念，是⼀种可以按照⾃⼰的期望，⾃⾏选择与之交换信息的节点的通信⽅式gossip, or anto-entropy, is an attractive way of replicating state that does not have strong consistency requirements2. 算法描述假设有 {p, q, ...} 为协议参与者。

每个参与者都有关于⼀个⾃⼰信息的表。

⽤编程语⾔可以描述为：记 InfoMap = Map<Key, (Value, Version)>，那么每个参与者要维护⼀个 InfoMap 类型的变量 localInfo。

同时每⼀个参与者要知道所有其他参与者的信息, 即要维护⼀个全局的表，即 Map<participant, InfoMap> 类型的变量 globalMap。

每个参与者更新⾃⼰的 localInfo，⽽由Gossip 协议负责将更新的信息同步到整个⽹络上。

每个节点和系统中的某些节点成为 peer (如果系统的规模⽐较⼩，和系统中所有的其他节点成为 peer)。

有三种不同的同步信息的⽅法：1）push-gossip: 最简单的情况下，⼀个节点 p 向 q 发送整个 GlobalMap2）pull-gossip: p 向 q 发送 digest, q 根据 digest 向 p 发送 p 过期的 (key, (value, version)) 列表3）push-pull-gossip:与pull-gossip类似，只是多了⼀步，A再将本地⽐B新的数据推送给B，B更新本地3. 特点gossip不要求节点知道所有其他节点，因此具有去中⼼化的特点，节点之间完全对等，不需要任何的中⼼节点。

gossip算法⼜被称为反熵（Anti-Entropy），熵是物理学上的⼀个概念，代表杂乱⽆章，⽽反熵就是在杂乱⽆章中寻求⼀致，这充分说明了Gossip的特点：在⼀个有界⽹络中，每个节点都随机地与其他节点通信，经过⼀番杂乱⽆章的通信，最终所有节点的状态都会达成⼀致。

⽹络传输协议有哪些注明：以下内容复制于百度知道，本⼈⽤于学习。

TCP/IP，互联⽹传输协议。

以下为各种⽹络传输协议列表(后⾯数字表⽰应⽤层协议默认服务端⼝)：AARP (ARP Address Resolution Protocol)BBGP (边缘⽹关协议 Border Gateway Protocol)蓝⽛(Blue Tooth)BOOTP (Bootstrap Protocol)DDHCP( Dynamic Host Configuration Protocol)DNS(域名服务 Domain Name Service)DVMRP (Distance-Vector Multicast Routing Protocol)EEGP (Exterior Gateway Protocol)FFTP (⽂件传输协议 File Transfer Protocol) 21HHDLC (⾼级数据链路控制协议 High-level Data Link Control)HELLO(routing protocol)HTTP 超⽂本传输协议 80HTTPS 安全超级⽂本传输协议IICMP (互联⽹控制报⽂协议 Internet Control Message Protocol)IDRP (InterDomain Routing Protocol)IEEE 802IGMP (Internet Group Management Protocol)IGP (内部⽹关协议 Interior Gateway Protocol )IMAPIP (互联⽹协议 Internet Protocol)IPXIS-IS(Intermediate System to Intermediate System Protocol)LLCP (链路控制协议 Link Control Protocol)LLC (逻辑链路控制协议 Logical Link Control)MMLD (多播监听发现协议 Multicast Listener Discovery)NNCP (⽹络控制协议 Network Control Protocol)NNTP (⽹络新闻传输协议 Network News Transfer Protocol) 119NTP (Network Time Protocol)PPPP (点对点协议 Protocol)POP (邮局协议 Post Office Protocol) 110RRARP (逆向地址解析协议 Reverse Address Resolution Protocol)RIP (路由信息协议 Routing Information Protocol)SSLIP (串⾏链路连接协议Serial Link Internet Protocol)SNMP (简单⽹络管理协议 Simple Network Management Protocol)SMTP (简单邮件传输协议 Simple Mail Transport Protocol) 25SCTP(流控制传输协议 Stream Control Transmission Protocol)TTCP (传输控制协议 Transmission Control Protocol)TFTP (Trivial File Transfer Protocol)Telnet (远程终端协议 remote terminal protocol) 23UUDP (⽤户数据报协议 User Datagram Protocol)常⽤的有以下⼏种：ARP(Address Resolution Protocol)地址解析协议 它是⽤于映射计算机的物理地址和临时指定的⽹络地址。

1.CE1和CE2属于同一个VPN，VPN实例的名字为vpna。

通过OptionC方式一实现CE1和CE2互通。

为实现该需求，ASBR-PE1配置了两条路由策略，针对1.[ASBR-PE1-bgp]peer route-policy policy2 export（仅填写IP地址）。

【答案】：(10.0.34.4/10.0.4.4)1.telemetry两种订阅方式，其中订阅时间较短的方式是_____?（全称）【答案】：(动态订阅)1.<rpc xm/ns ="xxxx"message-id="1024“属于netconf中的_____层（中文全称）【答案】：(消息)1.请将以下命令与其作用对应起来_______。

【答案】：(Arp br adcast-Suppress enable ---- ARP广播抑制Arp c llect h st enable ---- 主机信息搜集Arp-pr xy l cal enable ---- ARP本地代理Arp distribute-gateway enable ---- 分布式网关)1.Q S在执行时有一定的顺序,请将以下的几个Q S功能模块按照正确的顺序排序。

1-复杂流分类、2-拥塞避免、3-拥塞管理、4-简单流分类、5-流量整形1.CE1 和CE2 属于同一个WPN,VPN 实例的名字为vpna。

通过OptionC 方式一实现CE1 和CE2 互通。

为实现该需求，请将以下命令行与设备编号进行匹配。

1.HQoS 一共有三级队列: Leve1l, Level2, Level3。

请将以下队列名称与队列等级一一对应。

【答案】：1. NETC NE内容层为设备配置数据，针对以下NETC NEF信息,描述正确的有哪些项?A、该配置采用了Huawei-YANG方式B、该配置为在设备上创建VLAN 10C、该配置采用了NETC NF <edit-c nfig>操作,把配置数据加载到启动配置库D、<c nfig>中包含了“perati n”属性,为merge操作【答案】：ABCD1.SR-MPLS P licy可以借助BGP扩展来传递隧道信息，其信息如图所示。

An Overview of Recent Progress in the Study of Distributed Multi-agent CoordinationYongcan Cao,Member,IEEE,Wenwu Yu,Member,IEEE,Wei Ren,Member,IEEE,and Guanrong Chen,Fellow,IEEEAbstract—This article reviews some main results and progress in distributed multi-agent coordination,focusing on papers pub-lished in major control systems and robotics journals since 2006.Distributed coordination of multiple vehicles,including unmanned aerial vehicles,unmanned ground vehicles and un-manned underwater vehicles,has been a very active research subject studied extensively by the systems and control community. The recent results in this area are categorized into several directions,such as consensus,formation control,optimization, and estimation.After the review,a short discussion section is included to summarize the existing research and to propose several promising research directions along with some open problems that are deemed important for further investigations.Index Terms—Distributed coordination,formation control,sen-sor networks,multi-agent systemI.I NTRODUCTIONC ONTROL theory and practice may date back to thebeginning of the last century when Wright Brothers attempted theirfirst testflight in1903.Since then,control theory has gradually gained popularity,receiving more and wider attention especially during the World War II when it was developed and applied tofire-control systems,missile nav-igation and guidance,as well as various electronic automation devices.In the past several decades,modern control theory was further advanced due to the booming of aerospace technology based on large-scale engineering systems.During the rapid and sustained development of the modern control theory,technology for controlling a single vehicle, albeit higher-dimensional and complex,has become relatively mature and has produced many effective tools such as PID control,adaptive control,nonlinear control,intelligent control, This work was supported by the National Science Foundation under CAREER Award ECCS-1213291,the National Natural Science Foundation of China under Grant No.61104145and61120106010,the Natural Science Foundation of Jiangsu Province of China under Grant No.BK2011581,the Research Fund for the Doctoral Program of Higher Education of China under Grant No.20110092120024,the Fundamental Research Funds for the Central Universities of China,and the Hong Kong RGC under GRF Grant CityU1114/11E.The work of Yongcan Cao was supported by a National Research Council Research Associateship Award at AFRL.Y.Cao is with the Control Science Center of Excellence,Air Force Research Laboratory,Wright-Patterson AFB,OH45433,USA.W.Yu is with the Department of Mathematics,Southeast University,Nanjing210096,China and also with the School of Electrical and Computer Engineering,RMIT University,Melbourne VIC3001,Australia.W.Ren is with the Department of Electrical Engineering,University of California,Riverside,CA92521,USA.G.Chen is with the Department of Electronic Engineering,City University of Hong Kong,Hong Kong SAR,China.Copyright(c)2009IEEE.Personal use of this material is permitted. However,permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubs-permissions@.and robust control methodologies.In the past two decades in particular,control of multiple vehicles has received increas-ing demands spurred by the fact that many benefits can be obtained when a single complicated vehicle is equivalently replaced by multiple yet simpler vehicles.In this endeavor, two approaches are commonly adopted for controlling multiple vehicles:a centralized approach and a distributed approach. The centralized approach is based on the assumption that a central station is available and powerful enough to control a whole group of vehicles.Essentially,the centralized ap-proach is a direct extension of the traditional single-vehicle-based control philosophy and strategy.On the contrary,the distributed approach does not require a central station for control,at the cost of becoming far more complex in structure and organization.Although both approaches are considered practical depending on the situations and conditions of the real applications,the distributed approach is believed more promising due to many inevitable physical constraints such as limited resources and energy,short wireless communication ranges,narrow bandwidths,and large sizes of vehicles to manage and control.Therefore,the focus of this overview is placed on the distributed approach.In distributed control of a group of autonomous vehicles,the main objective typically is to have the whole group of vehicles working in a cooperative fashion throughout a distributed pro-tocol.Here,cooperative refers to a close relationship among all vehicles in the group where information sharing plays a central role.The distributed approach has many advantages in achieving cooperative group performances,especially with low operational costs,less system requirements,high robustness, strong adaptivity,andflexible scalability,therefore has been widely recognized and appreciated.The study of distributed control of multiple vehicles was perhapsfirst motivated by the work in distributed comput-ing[1],management science[2],and statistical physics[3]. In the control systems society,some pioneering works are generally referred to[4],[5],where an asynchronous agree-ment problem was studied for distributed decision-making problems.Thereafter,some consensus algorithms were studied under various information-flow constraints[6]–[10].There are several journal special issues on the related topics published af-ter2006,including the IEEE Transactions on Control Systems Technology(vol.15,no.4,2007),Proceedings of the IEEE (vol.94,no.4,2007),ASME Journal of Dynamic Systems, Measurement,and Control(vol.129,no.5,2007),SIAM Journal of Control and Optimization(vol.48,no.1,2009),and International Journal of Robust and Nonlinear Control(vol.21,no.12,2011).In addition,there are some recent reviewsand progress reports given in the surveys[11]–[15]and thebooks[16]–[23],among others.This article reviews some main results and recent progressin distributed multi-agent coordination,published in majorcontrol systems and robotics journals since2006.Due to space limitations,we refer the readers to[24]for a more completeversion of the same overview.For results before2006,thereaders are referred to[11]–[14].Specifically,this article reviews the recent research resultsin the following directions,which are not independent but actually may have overlapping to some extent:1.Consensus and the like(synchronization,rendezvous).Consensus refers to the group behavior that all theagents asymptotically reach a certain common agreementthrough a local distributed protocol,with or without predefined common speed and orientation.2.Distributed formation and the like(flocking).Distributedformation refers to the group behavior that all the agents form a pre-designed geometrical configuration throughlocal interactions with or without a common reference.3.Distributed optimization.This refers to algorithmic devel-opments for the analysis and optimization of large-scaledistributed systems.4.Distributed estimation and control.This refers to dis-tributed control design based on local estimation aboutthe needed global information.The rest of this article is organized as follows.In Section II,basic notations of graph theory and stochastic matrices are introduced.Sections III,IV,V,and VI describe the recentresearch results and progress in consensus,formation control, optimization,and estimation.Finally,the article is concludedby a short section of discussions with future perspectives.II.P RELIMINARIESA.Graph TheoryFor a system of n connected agents,its network topology can be modeled as a directed graph denoted by G=(V,W),where V={v1,v2,···,v n}and W⊆V×V are,respectively, the set of agents and the set of edges which directionallyconnect the agents together.Specifically,the directed edgedenoted by an ordered pair(v i,v j)means that agent j can access the state information of agent i.Accordingly,agent i is a neighbor of agent j.A directed path is a sequence of directed edges in the form of(v1,v2),(v2,v3),···,with all v i∈V.A directed graph has a directed spanning tree if there exists at least one agent that has a directed path to every other agent.The union of a set of directed graphs with the same setof agents,{G i1,···,G im},is a directed graph with the sameset of agents and its set of edges is given by the union of the edge sets of all the directed graphs G ij,j=1,···,m.A complete directed graph is a directed graph in which each pair of distinct agents is bidirectionally connected by an edge,thus there is a directed path from any agent to any other agent in the network.Two matrices are used to represent the network topology: the adjacency matrix A=[a ij]∈R n×n with a ij>0if (v j,v i)∈W and a ij=0otherwise,and the Laplacian matrix L=[ℓij]∈R n×n withℓii= n j=1a ij andℓij=−a ij,i=j, which is generally asymmetric for directed graphs.B.Stochastic MatricesA nonnegative square matrix is called(row)stochastic matrix if its every row is summed up to one.The product of two stochastic matrices is still a stochastic matrix.A row stochastic matrix P∈R n×n is called indecomposable and aperiodic if lim k→∞P k=1y T for some y∈R n[25],where 1is a vector with all elements being1.III.C ONSENSUSConsider a group of n agents,each with single-integrator kinematics described by˙x i(t)=u i(t),i=1,···,n,(1) where x i(t)and u i(t)are,respectively,the state and the control input of the i th agent.A typical consensus control algorithm is designed asu i(t)=nj=1a ij(t)[x j(t)−x i(t)],(2)where a ij(t)is the(i,j)th entry of the corresponding ad-jacency matrix at time t.The main idea behind(2)is that each agent moves towards the weighted average of the states of its neighbors.Given the switching network pattern due to the continuous motions of the dynamic agents,coupling coefficients a ij(t)in(2),hence the graph topologies,are generally time-varying.It is shown in[9],[10]that consensus is achieved if the underlying directed graph has a directed spanning tree in some jointly fashion in terms of a union of its time-varying graph topologies.The idea behind consensus serves as a fundamental principle for the design of distributed multi-agent coordination algo-rithms.Therefore,investigating consensus has been a main research direction in the study of distributed multi-agent co-ordination.To bridge the gap between the study of consensus algorithms and many physical properties inherited in practical systems,it is necessary and meaningful to study consensus by considering many practical factors,such as actuation,control, communication,computation,and vehicle dynamics,which characterize some important features of practical systems.This is the main motivation to study consensus.In the following part of the section,an overview of the research progress in the study of consensus is given,regarding stochastic network topologies and dynamics,complex dynamical systems,delay effects,and quantization,mainly after2006.Several milestone results prior to2006can be found in[2],[4]–[6],[8]–[10], [26].A.Stochastic Network Topologies and DynamicsIn multi-agent systems,the network topology among all vehicles plays a crucial role in determining consensus.The objective here is to explicitly identify necessary and/or suffi-cient conditions on the network topology such that consensus can be achieved under properly designed algorithms.It is often reasonable to consider the case when the network topology is deterministic under ideal communication chan-nels.Accordingly,main research on the consensus problem was conducted under a deterministicfixed/switching network topology.That is,the adjacency matrix A(t)is deterministic. Some other times,when considering random communication failures,random packet drops,and communication channel instabilities inherited in physical communication channels,it is necessary and important to study consensus problem in the stochastic setting where a network topology evolves according to some random distributions.That is,the adjacency matrix A(t)is stochastically evolving.In the deterministic setting,consensus is said to be achieved if all agents eventually reach agreement on a common state. In the stochastic setting,consensus is said to be achieved almost surely(respectively,in mean-square or in probability)if all agents reach agreement on a common state almost surely (respectively,in mean-square or with probability one).Note that the problem studied in the stochastic setting is slightly different from that studied in the deterministic setting due to the different assumptions in terms of the network topology. Consensus over a stochastic network topology was perhaps first studied in[27],where some sufficient conditions on the network topology were given to guarantee consensus with probability one for systems with single-integrator kinemat-ics(1),where the rate of convergence was also studied.Further results for consensus under a stochastic network topology were reported in[28]–[30],where research effort was conducted for systems with single-integrator kinematics[28],[29]or double-integrator dynamics[30].Consensus for single-integrator kine-matics under stochastic network topology has been exten-sively studied in particular,where some general conditions for almost-surely consensus was derived[29].Loosely speaking, almost-surely consensus for single-integrator kinematics can be achieved,i.e.,x i(t)−x j(t)→0almost surely,if and only if the expectation of the network topology,namely,the network topology associated with expectation E[A(t)],has a directed spanning tree.It is worth noting that the conditions are analogous to that in[9],[10],but in the stochastic setting. In view of the special structure of the closed-loop systems concerning consensus for single-integrator kinematics,basic properties of the stochastic matrices play a crucial role in the convergence analysis of the associated control algorithms. Consensus for double-integrator dynamics was studied in[30], where the switching network topology is assumed to be driven by a Bernoulli process,and it was shown that consensus can be achieved if the union of all the graphs has a directed spanning tree.Apparently,the requirement on the network topology for double-integrator dynamics is a special case of that for single-integrator kinematics due to the difference nature of thefinal states(constantfinal states for single-integrator kinematics and possible dynamicfinal states for double-integrator dynamics) caused by the substantial dynamical difference.It is still an open question as if some general conditions(corresponding to some specific algorithms)can be found for consensus with double-integrator dynamics.In addition to analyzing the conditions on the network topology such that consensus can be achieved,a special type of consensus algorithm,the so-called gossip algorithm[31],[32], has been used to achieve consensus in the stochastic setting. The gossip algorithm can always guarantee consensus almost surely if the available pairwise communication channels satisfy certain conditions(such as a connected graph).The way of network topology switching does not play any role in the consideration of consensus.The current study on consensus over stochastic network topologies has shown some interesting results regarding:(1) consensus algorithm design for various multi-agent systems,(2)conditions of the network topologies on consensus,and(3)effects of the stochastic network topologies on the con-vergence rate.Future research on this topic includes,but not limited to,the following two directions:(1)when the network topology itself is stochastic,how to determine the probability of reaching consensus almost surely?(2)compared with the deterministic network topology,what are the advantages and disadvantages of the stochastic network topology,regarding such as robustness and convergence rate?As is well known,disturbances and uncertainties often exist in networked systems,for example,channel noise,commu-nication noise,uncertainties in network parameters,etc.In addition to the stochastic network topologies discussed above, the effect of stochastic disturbances[33],[34]and uncertain-ties[35]on the consensus problem also needs investigation. Study has been mainly devoted to analyzing the performance of consensus algorithms subject to disturbances and to present-ing conditions on the uncertainties such that consensus can be achieved.In addition,another interesting direction in dealing with disturbances and uncertainties is to design distributed localfiltering algorithms so as to save energy and improve computational efficiency.Distributed localfiltering algorithms play an important role and are more effective than traditional centralizedfiltering algorithms for multi-agent systems.For example,in[36]–[38]some distributed Kalmanfilters are designed to implement data fusion.In[39],by analyzing consensus and pinning control in synchronization of complex networks,distributed consensusfiltering in sensor networks is addressed.Recently,Kalmanfiltering over a packet-dropping network is designed through a probabilistic approach[40]. Today,it remains a challenging problem to incorporate both dynamics of consensus and probabilistic(Kalman)filtering into a unified framework.plex Dynamical SystemsSince consensus is concerned with the behavior of a group of vehicles,it is natural to consider the system dynamics for practical vehicles in the study of the consensus problem. Although the study of consensus under various system dynam-ics is due to the existence of complex dynamics in practical systems,it is also interesting to observe that system dynamics play an important role in determining thefinal consensus state.For instance,the well-studied consensus of multi-agent systems with single-integrator kinematics often converges to a constantfinal value instead.However,consensus for double-integrator dynamics might admit a dynamicfinal value(i.e.,a time function).These important issues motivate the study of consensus under various system dynamics.As a direct extension of the study of the consensus prob-lem for systems with simple dynamics,for example,with single-integrator kinematics or double-integrator dynamics, consensus with general linear dynamics was also studied recently[41]–[43],where research is mainly devoted tofinding feedback control laws such that consensus(in terms of the output states)can be achieved for general linear systems˙x i=Ax i+Bu i,y i=Cx i,(3) where A,B,and C are constant matrices with compatible sizes.Apparently,the well-studied single-integrator kinematics and double-integrator dynamics are special cases of(3)for properly choosing A,B,and C.As a further extension,consensus for complex systems has also been extensively studied.Here,the term consensus for complex systems is used for the study of consensus problem when the system dynamics are nonlinear[44]–[48]or with nonlinear consensus algorithms[49],[50].Examples of the nonlinear system dynamics include:•Nonlinear oscillators[45].The dynamics are often as-sumed to be governed by the Kuramoto equation˙θi=ωi+Kstability.A well-studied consensus algorithm for(1)is given in(2),where it is now assumed that time delay exists.Two types of time delays,communication delay and input delay, have been considered in the munication delay accounts for the time for transmitting information from origin to destination.More precisely,if it takes time T ij for agent i to receive information from agent j,the closed-loop system of(1)using(2)under afixed network topology becomes˙x i(t)=nj=1a ij(t)[x j(t−T ij)−x i(t)].(7)An interpretation of(7)is that at time t,agent i receives information from agent j and uses data x j(t−T ij)instead of x j(t)due to the time delay.Note that agent i can get its own information instantly,therefore,input delay can be considered as the summation of computation time and execution time. More precisely,if the input delay for agent i is given by T p i, then the closed-loop system of(1)using(2)becomes˙x i(t)=nj=1a ij(t)[x j(t−T p i)−x i(t−T p i)].(8)Clearly,(7)refers to the case when only communication delay is considered while(8)refers to the case when only input delay is considered.It should be emphasized that both communication delay and input delay might be time-varying and they might co-exist at the same time.In addition to time delay,it is also important to consider packet drops in exchanging state information.Fortunately, consensus with packet drops can be considered as a special case of consensus with time delay,because re-sending packets after they were dropped can be easily done but just having time delay in the data transmission channels.Thus,the main problem involved in consensus with time delay is to study the effects of time delay on the convergence and performance of consensus,referred to as consensusabil-ity[52].Because time delay might affect the system stability,it is important to study under what conditions consensus can still be guaranteed even if time delay exists.In other words,can onefind conditions on the time delay such that consensus can be achieved?For this purpose,the effect of time delay on the consensusability of(1)using(2)was investigated.When there exists only(constant)input delay,a sufficient condition on the time delay to guarantee consensus under afixed undirected interaction graph is presented in[8].Specifically,an upper bound for the time delay is derived under which consensus can be achieved.This is a well-expected result because time delay normally degrades the system performance gradually but will not destroy the system stability unless the time delay is above a certain threshold.Further studies can be found in, e.g.,[53],[54],which demonstrate that for(1)using(2),the communication delay does not affect the consensusability but the input delay does.In a similar manner,consensus with time delay was studied for systems with different dynamics, where the dynamics(1)are replaced by other more complex ones,such as double-integrator dynamics[55],[56],complex networks[57],[58],rigid bodies[59],[60],and general nonlinear dynamics[61].In summary,the existing study of consensus with time delay mainly focuses on analyzing the stability of consensus algo-rithms with time delay for various types of system dynamics, including linear and nonlinear dynamics.Generally speaking, consensus with time delay for systems with nonlinear dynam-ics is more challenging.For most consensus algorithms with time delays,the main research question is to determine an upper bound of the time delay under which time delay does not affect the consensusability.For communication delay,it is possible to achieve consensus under a relatively large time delay threshold.A notable phenomenon in this case is that thefinal consensus state is constant.Considering both linear and nonlinear system dynamics in consensus,the main tools for stability analysis of the closed-loop systems include matrix theory[53],Lyapunov functions[57],frequency-domain ap-proach[54],passivity[58],and the contraction principle[62]. Although consensus with time delay has been studied extensively,it is often assumed that time delay is either constant or random.However,time delay itself might obey its own dynamics,which possibly depend on the communication distance,total computation load and computation capability, etc.Therefore,it is more suitable to represent the time delay as another system variable to be considered in the study of the consensus problem.In addition,it is also important to consider time delay and other physical constraints simultaneously in the study of the consensus problem.D.QuantizationQuantized consensus has been studied recently with motiva-tion from digital signal processing.Here,quantized consensus refers to consensus when the measurements are digital rather than analog therefore the information received by each agent is not continuous and might have been truncated due to digital finite precision constraints.Roughly speaking,for an analog signal s,a typical quantizer with an accuracy parameterδ, also referred to as quantization step size,is described by Q(s)=q(s,δ),where Q(s)is the quantized signal and q(·,·) is the associated quantization function.For instance[63],a quantizer rounding a signal s to its nearest integer can be expressed as Q(s)=n,if s∈[(n−1/2)δ,(n+1/2)δ],n∈Z, where Z denotes the integer set.Note that the types of quantizers might be different for different systems,hence Q(s) may differ for different systems.Due to the truncation of the signals received,consensus is now considered achieved if the maximal state difference is not larger than the accuracy level associated with the whole system.A notable feature for consensus with quantization is that the time to reach consensus is usuallyfinite.That is,it often takes afinite period of time for all agents’states to converge to an accuracy interval.Accordingly,the main research is to investigate the convergence time associated with the proposed consensus algorithm.Quantized consensus was probablyfirst studied in[63], where a quantized gossip algorithm was proposed and its convergence was analyzed.In particular,the bound of theconvergence time for a complete graph was shown to be poly-nomial in the network size.In[64],coding/decoding strate-gies were introduced to the quantized consensus algorithms, where it was shown that the convergence rate depends on the accuracy of the quantization but not the coding/decoding schemes.In[65],quantized consensus was studied via the gossip algorithm,with both lower and upper bounds of the expected convergence time in the worst case derived in terms of the principle submatrices of the Laplacian matrix.Further results regarding quantized consensus were reported in[66]–[68],where the main research was also on the convergence time for various proposed quantized consensus algorithms as well as the quantization effects on the convergence time.It is intuitively reasonable that the convergence time depends on both the quantization level and the network topology.It is then natural to ask if and how the quantization methods affect the convergence time.This is an important measure of the robustness of a quantized consensus algorithm(with respect to the quantization method).Note that it is interesting but also more challenging to study consensus for general linear/nonlinear systems with quantiza-tion.Because the difference between the truncated signal and the original signal is bounded,consensus with quantization can be considered as a special case of one without quantization when there exist bounded disturbances.Therefore,if consensus can be achieved for a group of vehicles in the absence of quantization,it might be intuitively correct to say that the differences among the states of all vehicles will be bounded if the quantization precision is small enough.However,it is still an open question to rigorously describe the quantization effects on consensus with general linear/nonlinear systems.E.RemarksIn summary,the existing research on the consensus problem has covered a number of physical properties for practical systems and control performance analysis.However,the study of the consensus problem covering multiple physical properties and/or control performance analysis has been largely ignored. In other words,two or more problems discussed in the above subsections might need to be taken into consideration simul-taneously when studying the consensus problem.In addition, consensus algorithms normally guarantee the agreement of a team of agents on some common states without taking group formation into consideration.To reflect many practical applications where a group of agents are normally required to form some preferred geometric structure,it is desirable to consider a task-oriented formation control problem for a group of mobile agents,which motivates the study of formation control presented in the next section.IV.F ORMATION C ONTROLCompared with the consensus problem where thefinal states of all agents typically reach a singleton,thefinal states of all agents can be more diversified under the formation control scenario.Indeed,formation control is more desirable in many practical applications such as formationflying,co-operative transportation,sensor networks,as well as combat intelligence,surveillance,and reconnaissance.In addition,theperformance of a team of agents working cooperatively oftenexceeds the simple integration of the performances of all individual agents.For its broad applications and advantages,formation control has been a very active research subject inthe control systems community,where a certain geometric pattern is aimed to form with or without a group reference.More precisely,the main objective of formation control is to coordinate a group of agents such that they can achievesome desired formation so that some tasks can befinished bythe collaboration of the agents.Generally speaking,formation control can be categorized according to the group reference.Formation control without a group reference,called formationproducing,refers to the algorithm design for a group of agents to reach some pre-desired geometric pattern in the absenceof a group reference,which can also be considered as the control objective.Formation control with a group reference,called formation tracking,refers to the same task but followingthe predesignated group reference.Due to the existence of the group reference,formation tracking is usually much morechallenging than formation producing and control algorithmsfor the latter might not be useful for the former.As of today, there are still many open questions in solving the formationtracking problem.The following part of the section reviews and discussesrecent research results and progress in formation control, including formation producing and formation tracking,mainlyaccomplished after2006.Several milestone results prior to 2006can be found in[69]–[71].A.Formation ProducingThe existing work in formation control aims at analyzingthe formation behavior under certain control laws,along with stability analysis.1)Matrix Theory Approach:Due to the nature of multi-agent systems,matrix theory has been frequently used in thestability analysis of their distributed coordination.Note that consensus input to each agent(see e.g.,(2))isessentially a weighted average of the differences between the states of the agent’s neighbors and its own.As an extensionof the consensus algorithms,some coupling matrices wereintroduced here to offset the corresponding control inputs by some angles[72],[73].For example,given(1),the controlinput(2)is revised as u i(t)= n j=1a ij(t)C[x j(t)−x i(t)], where C is a coupling matrix with compatible size.If x i∈R3, then C can be viewed as the3-D rotational matrix.The mainidea behind the revised algorithm is that the original controlinput for reaching consensus is now rotated by some angles. The closed-loop system can be expressed in a vector form, whose stability can be determined by studying the distribution of the eigenvalues of a certain transfer matrix.Main research work was conducted in[72],[73]to analyze the collective motions for systems with single-integrator kinematics and double-integrator dynamics,where the network topology,the damping gain,and C were shown to affect the collective motions.Analogously,the collective motions for a team of nonlinear self-propelling agents were shown to be affected by。

高中英语作文博客必须尽快实名制(Bloggers must soongive real names)hu qiheng, chairman of the board of directors of the isc, was reported to have said on tuesday at info china 2010 in beijing that china is making attempts to strike a balance between individual privacy and public interests.”the past understanding of privacy is too absolute. not only china, but also the whole world, should realize the necessity of balancing individual privacy and public and national interests,” he said.a new system is likely to be adopted, requiring chinese netizens to submit information like real names and id card numbers when they register a blog or a bbs (bulletin board service) izens will be able to continue choosing their own online name, and as long as they do not violate laws their personal information will remain private and safe.the first area for real name application will be blogs, a popular form of internet-based diary.blogs have been used by somepeople to infringe upon other people’s privacy and rights.for example an infamous tv host had thousands of netizens visit her blog just because she wrote an article about a well-known tv anchor’s marriage history, which included some allegedly false information.as a blogger’s real name is unknown, it is very difficult to safeguard privacy and rights.the society, affiliated to the ministry of information industry, was entrusted by the ministry to form a blog research panel to provide solutions for the development of china’s blog industry.the real name system is said to be able to protect law-abiding netizens’privacy.yang junzuo, secretary-general of isc’s self-discipline working commission, was quoted by beijing-based china times a month ago saying that the real name system is the solution.”free speech on the internet does not include talking nonsense and not taking responsibility. bad symptoms will be curbed,” he was quoted as saying.however, not many netizens support the system.an online poll at yesterday showed that only one quarter of surveyed netizens agreed that the system would crack down on online CRImes while not interfering with internet use.more than 70 per cent of people were against it, believingit was “absurd” to enforce a real name system just because of a minority of people who committed online CRImes.xinhua news agency reported that hu stressed at the meeting that the society would adopt multiple ways to improve the internet environment.hu was quoted as saying that the direct purpose of improving the internet environment is to enable the young generation to grow up in an internet-friendly environment like youth in developed countries.。

分布式原理：一文了解Gossip 协议gossip 协议（gossip protocol）又称epidemic 协议（epidemic protocol），是基于流行病传播方式的节点或者进程之间信息交换的协议，在分布式系统中被广泛使用，比如我们可以使用gossip 协议来确保网络中所有节点的数据一样。

从gossip 单词就可以看到，其中文意思是八卦、流言等意思，我们可以想象下绯闻的传播（或者流行病的传播）；gossip 协议的工作原理就类似于这个。

gossip 协议利用一种随机的方式将信息传播到整个网络中，并在一定时间内使得系统内的所有节点数据一致。

Gossip 其实是一种去中心化思路的分布式协议，解决状态在集群中的传播和状态一致性的保证两个问题。

gossip 优势可扩展性（Scalable）gossip 协议是可扩展的，一般需要O（logN）轮就可以将信息传播到所有的节点，其中N 代表节点的个数。

每个节点仅发送固定数量的消息，并且与网络中节点数目无法。

在数据传送的时候，节点并不会等待消息的ack，所以消息传送失败也没有关系，因为可以通过其他节点将消息传递给之前传送失败的节点。

系统可以轻松扩展到数百万个进程。

容错（Fault-tolerance）网络中任何节点的重启或者宕机都不会影响gossip 协议的运行。

健壮性（Robust）gossip 协议是去中心化的协议，所以集群中的所有节点都是对等的，没有特殊的节点，所以任何节点出现问题都不会阻止其他节点继续发送消息。

任何节点都可以随时加入或离开，而不会影响系统的整体服务质量（QOS）最终一致性（Convergent consistency）Gossip 协议实现信息指数级的快速传播，因此在有新信息需要传播时，消息可以快速地发送到全局节点，在有限的时间内能够做到所有节点都拥有最新的数据。

gossip 协议的类型。

异质信息网络的互信息最大化社区搜索异质信息网络的互信息最大化社区搜索随着信息技术的不断发展，人们对于社交网络的研究也越来越深入。

在社交网络中，人们通过互相交流分享信息，形成了一个庞大而复杂的信息网络。

然而，传统的社区搜索算法在处理这种异质信息网络时面临一些挑战，例如节点类型多样性和节点之间边的异构性。

因此，研究人员提出了基于互信息最大化的社区搜索方法，可以有效地发现异质信息网络中的社区结构。

互信息最大化是一种用于发现社区结构的无监督学习方法，它通过计算节点对之间的互信息来衡量它们之间的相关性。

在异质信息网络中，节点之间可能存在多种类型的关系，例如用户之间的社交关系、物品与用户之间的交互关系等。

传统的社区搜索算法往往忽视了这些节点类型的差异，导致无法准确地发现社区结构。

而互信息最大化的方法能够利用节点之间的关系和节点的属性信息，有效地挖掘不同类型节点之间的关联性。

在互信息最大化的社区搜索方法中，首先需要构建节点之间的关系网络。

异质信息网络中的节点可以表示为多维向量，每个维度对应一个节点类型。

节点之间的关系可以表示为一个矩阵，其中每个元素表示两个节点之间的互信息。

然后，通过最大化节点对之间的互信息，可以找到具有相关性的节点对，进而聚类形成社区结构。

通过不断调整聚类的阈值，可以得到不同规模的社区。

与传统的社区搜索算法相比，互信息最大化的方法具有以下优势。

首先，它能够考虑节点之间的多样性，不仅仅局限于某一类型的节点。

这样可以更全面地发现社区结构。

其次，互信息最大化的方法可以充分利用节点的属性信息，例如节点的特征向量、标签等。

这样可以提供更具有解释性的社区结果。

此外，互信息最大化的方法还能够适应异质信息网络的动态变化，对于新增节点或者关系的处理更加灵活。

然而，互信息最大化的社区搜索方法也面临一些挑战。

首先，节点之间的关系网络构建需要耗费大量的计算资源和存储空间。

同时，由于异质信息网络的复杂性，目前还缺乏有效的算法来准确地计算节点对之间的互信息。

产业观察INDUSTRY OBSERVATIONS32信息安全与通信保密 SEPT2020阿伦·维什瓦纳特（Arun Vishwanath）博士毕业于哈佛大学，是网络安全研究和咨询公司Avant Research Group（ARG）的首席技术官、美国国家安全局安全与隐私科学理事会杰出专家小组成员，曾多次受邀出席美国参议院/众议院的全球安全会议，首次强调了用户责任的必要性，从发展网络卫生到更安全地使用网络，以避免受到网络攻击。

迄今为止，他发表近50篇关于技术用户和网络安全问题的文章，其中，关于提高网络抵御在线社交工程能力的研究得到了美国国家科学基金会的资助，研究成果已提交给世界各地的国家安全和执法机构的负责人。

来自社会工程角度的网络脆弱性思考——访网络安全和网络隐私领域的权威专家阿伦·维什瓦纳特博士阿伦·维什瓦纳特博士侧重于提高个人、组织和国家对网络攻击的抵御能力的研究，重点关注网络安全中最薄弱的环节，即我们所有的互联网用户。

他是第一个证明用户认知作用的研究者，特别是有关用户如何处理信息和网络风险，其研究首次强调社交媒体的危险性，而且还是第一个展示基于移动的社交工程攻击的威胁的研究人员。

阿伦·维什瓦纳特博士还扮演着技术专家的角色，出于对公共利益的考虑，他撰写和强调了网络安全方面的问题及其解决方案。

他的许多独创性想法导致了新产品、新工艺和新政策的产生，对网络安全科学的研究和观点也曾在《连线》杂志、《今日美国》、Politico、CNN、《华盛顿邮报》、《科学美国人》以及其他数百家国内外新闻机构上发表过专题报道。

安吉拉·斯科特·布里格斯：您是如何成为人类网络脆弱性和社会工程领域的世界顶级专家的？阿伦·维什瓦纳特：我是一名受过训练的社会科学家，我对网络安全的兴趣来自我在技术应用和心理学方面的研究工作。

我花了十年时间研究人们是如何安吉拉·斯科特·布里格斯■ 高端访谈栏目责编：李天婴33SEPT 2020 信息安全与通信保密产生创新的想法、技能、技术，以及人们如何接受、拒绝、利用或错误地利用它们。